OpenRaiser/Intern-Atlas huggingface dataset:
Smaller Keys for the McEliece Cryptosystem: A Convolutional Variant with GRS Codes
Venue: arXiv cs.IT 2023
Year: 2023

In this paper we present a variant of the McEliece cryptosystem that possesses several interesting properties, including a reduction of the public key for a given security level. In contrast to the classical McEliece cryptosystems, where block codes are used, we propose the use of a convolutional encoder to be part of the public key. The permutation matrix is substituted by a polynomial matrix whose coefficient matrices have columns with weight zero or at least weight two. This allows the use of Generalized Reed-Solomon (GRS) codes which translates into shorter keys for a given security level. Hence, the private key is constituted by a generator matrix of a GRS code and two polynomial matrices containing large parts generated completely at random. In this setting the message is a sequence of messages instead of a single block message and the errors are added throughout the sequence. We discuss possible structural and ISD attacks to this scheme. We conclude presenting the key sizes obtained for different parameters and estimating the computational cost of encryption and decryption process.
Towards Handling Uncertainty-at-Source in AI -- A Review and Next Steps for Interval Regression
Venue: arXiv cs.LG 2023
Year: 2023

Most of statistics and AI draw insights through modelling discord or variance between sources of information (i.e., inter-source uncertainty). Increasingly, however, research is focusing upon uncertainty arising at the level of individual measurements (i.e., within- or intra-source), such as for a given sensor output or human response. Here, adopting intervals rather than numbers as the fundamental data-type provides an efficient, powerful, yet challenging way forward -- offering systematic capture of uncertainty-at-source, increasing informational capacity, and ultimately potential for insight. Following recent progress in the capture of interval-valued data, including from human participants, conducting machine learning directly upon intervals is a crucial next step. This paper focuses on linear regression for interval-valued data as a recent growth area, providing an essential foundation for broader use of intervals in AI. We conduct an in-depth analysis of state-of-the-art methods, elucidating their behaviour, advantages, and pitfalls when applied to datasets with different properties. Specific emphasis is given to the challenge of preserving mathematical coherence -- i.e., ensuring that models maintain fundamental mathematical properties of intervals throughout -- and the paper puts forward extensions to an existing approach to guarantee this. Carefully designed experiments, using both synthetic and real-world data, are conducted -- with findings presented alongside novel visualizations for interval-valued regression outputs, designed to maximise model interpretability. Finally, the paper makes recommendations concerning method suitability for data sets with specific properties and highlights remaining challenges and important next steps for developing AI with the capacity to handle uncertainty-at-source.
The Role of Cross-Silo Federated Learning in Facilitating Data Sharing in the Agri-Food Sector
Venue: arXiv cs.LG 2023
Year: 2023

Data sharing remains a major hindering factor when it comes to adopting emerging AI technologies in general, but particularly in the agri-food sector. Protectiveness of data is natural in this setting; data is a precious commodity for data owners, which if used properly can provide them with useful insights on operations and processes leading to a competitive advantage. Unfortunately, novel AI technologies often require large amounts of training data in order to perform well, something that in many scenarios is unrealistic. However, recent machine learning advances, e.g. federated learning and privacy-preserving technologies, can offer a solution to this issue via providing the infrastructure and underpinning technologies needed to use data from various sources to train models without ever sharing the raw data themselves. In this paper, we propose a technical solution based on federated learning that uses decentralized data, (i.e. data that are not exchanged or shared but remain with the owners) to develop a cross-silo machine learning model that facilitates data sharing across supply chains. We focus our data sharing proposition on improving production optimization through soybean yield prediction, and provide potential use-cases that such methods can assist in other problem settings. Our results demonstrate that our approach not only performs better than each of the models trained on an individual data source, but also that data sharing in the agri-food sector can be enabled via alternatives to data exchange, whilst also helping to adopt emerging machine learning technologies to boost productivity.
Approximating the Earth Mover's Distance between sets of geometric objects
Venue: arXiv cs.CG 2023
Year: 2023

Given two distributions $P$ and $S$ of equal total mass, the Earth Mover's Distance measures the cost of transforming one distribution into the other, where the cost of moving a unit of mass is equal to the distance over which it is moved. We give approximation algorithms for the Earth Mover's Distance between various sets of geometric objects. We give a $(1 + \varepsilon)$-approximation when $P$ is a set of weighted points and $S$ is a set of line segments, triangles or $d$-dimensional simplices. When $P$ and $S$ are both sets of line segments, sets of triangles or sets of simplices, we give a $(1 + \varepsilon)$-approximation with a small additive term. All algorithms run in time polynomial in the size of $P$ and $S$, and actually calculate the transport plan (that is, a specification of how to move the mass), rather than just the cost. To our knowledge, these are the first combinatorial algorithms with a provable approximation ratio for the Earth Mover's Distance when the objects are continuous rather than discrete points.
What's behind tight deadlines? Business causes of technical debt
Venue: arXiv cs.SE 2023
Year: 2023

What are the business causes behind tight deadlines? What drives the prioritization of features that pushes quality matters to the back burner? We conducted a survey with 71 experienced practitioners and did a thematic analysis of the open-ended answers to the question: ``Could you give examples of how business may contribute to technical debt?'' Business-related causes were organized into two categories: pure-business and business/IT gap, and they were related to `tight deadlines' and `features over quality', the most frequently cited management reasons for technical debt. We contribute a cause-effect model which relates the various business causes of tight deadlines and the behavior of prioritizing features over quality aspects.
Multi-Attention-Based Soft Partition Network for Vehicle Re-Identification
Venue: arXiv cs.CV 2023
Year: 2023

Vehicle re-identification helps in distinguishing between images of the same and other vehicles. It is a challenging process because of significant intra-instance differences between identical vehicles from different views and subtle inter-instance differences between similar vehicles. To solve this issue, researchers have extracted view-aware or part-specific features via spatial attention mechanisms, which usually result in noisy attention maps or otherwise require expensive additional annotation for metadata, such as key points, to improve the quality. Meanwhile, based on the researchers' insights, various handcrafted multi-attention architectures for specific viewpoints or vehicle parts have been proposed. However, this approach does not guarantee that the number and nature of attention branches will be optimal for real-world re-identification tasks. To address these problems, we proposed a new vehicle re-identification network based on a multiple soft attention mechanism for capturing various discriminative regions from different viewpoints more efficiently. Furthermore, this model can significantly reduce the noise in spatial attention maps by devising a new method for creating an attention map for insignificant regions and then excluding it from generating the final result. We also combined a channel-wise attention mechanism with a spatial attention mechanism for the efficient selection of important semantic attributes for vehicle re-identification. Our experiments showed that our proposed model achieved a state-of-the-art performance among the attention-based methods without metadata and was comparable to the approaches using metadata for the VehicleID and VERI-Wild datasets.
Lacon-, Shrub- and Parity-Decompositions: Characterizing Transductions of Bounded Expansion Classes
Venue: arXiv cs.DM 2023
Year: 2023

The concept of bounded expansion provides a robust way to capture sparse graph classes with interesting algorithmic properties. Most notably, every problem definable in first-order logic can be solved in linear time on bounded expansion graph classes. First-order interpretations and transductions of sparse graph classes lead to more general, dense graph classes that seem to inherit many of the nice algorithmic properties of their sparse counterparts. In this paper, we show that one can encode graphs from a class with structurally bounded expansion via lacon-, shrub- and parity-decompositions from a class with bounded expansion. These decompositions are useful for lifting properties from sparse to structurally sparse graph classes.
Separating Rank Logic from Polynomial Time
Venue: arXiv cs.LO 2023
Year: 2023

In the search for a logic capturing polynomial time the most promising candidates are Choiceless Polynomial Time (CPT) and rank logic. Rank logic extends fixed-point logic with counting by a rank operator over prime fields. We show that the isomorphism problem for CFI graphs over $\mathbb{Z}_{2^i}$ cannot be defined in rank logic, even if the base graph is totally ordered. However, CPT can define this isomorphism problem. We thereby separate rank logic from CPT and in particular from polynomial time.
D-VAL: An automatic functional equivalence validation tool for planning domain models
Venue: arXiv cs.AI 2023
Year: 2023

This paper introduces an approach to validate the functional equivalence of planning domain models. Validating the functional equivalence of planning domain models is the problem of formally confirming that two planning domain models can be used to solve the same set of problems for any set of objects. The need for techniques to validate the functional equivalence of planning domain models has been highlighted in previous research and has applications in model learning, development and extension. We prove the soundness and completeness of our method. We also develop D-VAL, an automatic functional equivalence validation tool for planning domain models. Empirical evaluation shows that D-VAL validates the functional equivalence of all examined domains in less than 43 seconds. Additionally, we provide a benchmark to evaluate the feasibility and performance of this and future related work.
The Factual Inconsistency Problem in Abstractive Text Summarization: A Survey
Venue: arXiv cs.CL 2023
Year: 2023

Recently, various neural encoder-decoder models pioneered by Seq2Seq framework have been proposed to achieve the goal of generating more abstractive summaries by learning to map input text to output text. At a high level, such neural models can freely generate summaries without any constraint on the words or phrases used. Moreover, their format is closer to human-edited summaries and output is more readable and fluent. However, the neural model's abstraction ability is a double-edged sword. A commonly observed problem with the generated summaries is the distortion or fabrication of factual information in the article. This inconsistency between the original text and the summary has caused various concerns over its applicability, and the previous evaluation methods of text summarization are not suitable for this issue. In response to the above problems, the current research direction is predominantly divided into two categories, one is to design fact-aware evaluation metrics to select outputs without factual inconsistency errors, and the other is to develop new summarization systems towards factual consistency. In this survey, we focus on presenting a comprehensive review of these fact-specific evaluation methods and text summarization models.
BLM-17m: A Large-Scale Dataset for Black Lives Matter Topic Detection on Twitter
Venue: arXiv cs.CL 2023
Year: 2023

Protection of human rights is one of the most important problems of our world. In this paper, our aim is to provide a dataset which covers one of the most significant human rights contradiction in recent months affected the whole world, George Floyd incident. We propose a labeled dataset for topic detection that contains 17 million tweets. These Tweets are collected from 25 May 2020 to 21 August 2020 that covers 89 days from start of this incident. We labeled the dataset by monitoring most trending news topics from global and local newspapers. Apart from that, we present two baselines, TF-IDF and LDA. We evaluated the results of these two methods with three different k values for metrics of precision, recall and f1-score. The collected dataset is available at https://github.com/MeysamAsgariC/BLMT.
Codes for the Z-channel
Venue: arXiv cs.IT 2023
Year: 2023

This paper is a collection of results on combinatorial properties of codes for the Z-channel. A Z-channel with error fraction $τ$ takes as input a length-$n$ binary codeword and injects in an adversarial manner up to $nτ$ asymmetric errors, i.e., errors that only zero out bits but do not flip $0$'s to $1$'s. It is known that the largest $(L-1)$-list-decodable code for the Z-channel with error fraction $τ$ has exponential size (in $n$) if $τ$ is less than a critical value that we call the $(L-1)$-list-decoding Plotkin point and has constant size if $τ$ is larger than the threshold. The $(L-1)$-list-decoding Plotkin point is known to be $ L^{-\frac{1}{L-1}} - L^{-\frac{L}{L-1}} $, which equals $1/4$ for unique-decoding with $ L-1=1 $. In this paper, we derive various results for the size of the largest codes above and below the list-decoding Plotkin point. In particular, we show that the largest $(L-1)$-list-decodable code $ε$-above the Plotkin point, {for any given sufficiently small positive constant $ ε>0 $,} has size $Θ_L(ε^{-3/2})$ for any $L-1\ge1$. We also devise upper and lower bounds on the exponential size of codes below the list-decoding Plotkin point.
Bandit based centralized matching in two-sided markets for peer to peer lending
Venue: arXiv cs.LG 2023
Year: 2023

Sequential fundraising in two sided online platforms enable peer to peer lending by sequentially bringing potential contributors, each of whose decisions impact other contributors in the market. However, understanding the dynamics of sequential contributions in online platforms for peer lending has been an open ended research question. The centralized investment mechanism in these platforms makes it difficult to understand the implicit competition that borrowers face from a single lender at any point in time. Matching markets are a model of pairing agents where the preferences of agents from both sides in terms of their preferred pairing for transactions can allow to decentralize the market. We study investment designs in two sided platforms using matching markets when the investors or lenders also face restrictions on the investments based on borrower preferences. This situation creates an implicit competition among the lenders in addition to the existing borrower competition, especially when the lenders are uncertain about their standing in the market and thereby the probability of their investments being accepted or the borrower loan requests for projects reaching the reserve price. We devise a technique based on sequential decision making that allows the lenders to adjust their choices based on the dynamics of uncertainty from competition over time. We simulate two sided market matchings in a sequential decision framework and show the dynamics of the lender regret amassed compared to the optimal borrower-lender matching and find that the lender regret depends on the initial preferences set by the lenders which could affect their learning over decision making steps.
Contrastive Attraction and Contrastive Repulsion for Representation Learning
Venue: arXiv cs.LG 2023
Year: 2023

Contrastive learning (CL) methods effectively learn data representations in a self-supervision manner, where the encoder contrasts each positive sample over multiple negative samples via a one-vs-many softmax cross-entropy loss. By leveraging large amounts of unlabeled image data, recent CL methods have achieved promising results when pretrained on large-scale datasets, such as ImageNet. However, most of them consider the augmented views from the same instance are positive pairs, while views from other instances are negative ones. Such binary partition insufficiently considers the relation between samples and tends to yield worse performance when generalized on images in the wild. In this paper, to further improve the performance of CL and enhance its robustness on various datasets, {we propose a doubly CL strategy that separately compares positive and negative samples within their own groups, and then proceeds with a contrast between positive and negative groups}. We realize this strategy with contrastive attraction and contrastive repulsion (CACR), which makes the query not only exert a greater force to attract more distant positive samples but also do so to repel closer negative samples. Theoretical analysis reveals that CACR generalizes CL's behavior by positive attraction and negative repulsion, and it further considers the intra-contrastive relation within the positive and negative pairs to narrow the gap between the sampled and true distribution, which is important when datasets are less curated. With our extensive experiments, CACR not only demonstrates good performance on CL benchmarks, but also shows better robustness when generalized on imbalanced image datasets. Code and pre-trained checkpoints are available at https://github.com/JegZheng/CACR-SSL.
Proximal Causal Learning with Kernels: Two-Stage Estimation and Moment Restriction
Venue: arXiv cs.LG 2023
Year: 2023

We address the problem of causal effect estimation in the presence of unobserved confounding, but where proxies for the latent confounder(s) are observed. We propose two kernel-based methods for nonlinear causal effect estimation in this setting: (a) a two-stage regression approach, and (b) a maximum moment restriction approach. We focus on the proximal causal learning setting, but our methods can be used to solve a wider class of inverse problems characterised by a Fredholm integral equation. In particular, we provide a unifying view of two-stage and moment restriction approaches for solving this problem in a nonlinear setting. We provide consistency guarantees for each algorithm, and we demonstrate these approaches achieve competitive results on synthetic data and data simulating a real-world task. In particular, our approach outperforms earlier methods that are not suited to leveraging proxy variables.
Improved Local Computation Algorithms for Constructing Spanners
Venue: arXiv cs.DS 2023
Year: 2023

A spanner of a graph is a subgraph that preserves lengths of shortest paths up to a multiplicative distortion. For every $k$, a spanner with size $O(n^{1+1/k})$ and stretch $(2k+1)$ can be constructed by a simple centralized greedy algorithm, and this is tight assuming Erdős girth conjecture. In this paper we study the problem of constructing spanners in a local manner, specifically in the Local Computation Model proposed by Rubinfeld et al. (ICS 2011). We provide a randomized Local Computation Agorithm (LCA) for constructing $(2r-1)$-spanners with $\tilde{O}(n^{1+1/r})$ edges and probe complexity of $\tilde{O}(n^{1-1/r})$ for $r \in \{2,3\}$, where $n$ denotes the number of vertices in the input graph. Up to polylogarithmic factors, in both cases, the stretch factor is optimal (for the respective number of edges). In addition, our probe complexity for $r=2$, i.e., for constructing a $3$-spanner, is optimal up to polylogarithmic factors. Our result improves over the probe complexity of Parter et al. (ITCS 2019) that is $\tilde{O}(n^{1-1/2r})$ for $r \in \{2,3\}$. Both our algorithms and the algorithms of Parter et al. use a combination of neighbor-probes and pair-probes in the above-mentioned LCAs. For general $k\geq 1$, we provide an LCA for constructing $O(k^2)$-spanners with $\tilde{O}(n^{1+1/k})$ edges using $O(n^{2/3}Δ^2)$ neighbor-probes, improving over the $\tilde{O}(n^{2/3}Δ^4)$ algorithm of Parter et al. By developing a new randomized LCA for graph decomposition, we further improve the probe complexity of the latter task to be $O(n^{2/3-(1.5-α)/k}Δ^2)$, for any constant $α>0$. This latter LCA may be of independent interest.
Human-Machine Interaction in the Light of Turing and Wittgenstein
Venue: arXiv cs.GL 2023
Year: 2023

We propose a study of the constitution of meaning in human-computer interaction based on Turing and Wittgenstein's definitions of thought, understanding, and decision. We show by the comparative analysis of the conceptual similarities and differences between the two authors that the common sense between humans and machines is co-constituted in and from action and that it is precisely in this co-constitution that lies the social value of their interaction. This involves problematizing human-machine interaction around the question of what it means to "follow a rule" to define and distinguish the interpretative modes and decision-making behaviors of each. We conclude that the mutualization of signs that takes place through the human-machine dialogue is at the foundation of the constitution of a computerized society.
On the probability of generating a primitive matrix
Venue: arXiv cs.SC 2023
Year: 2023

Given a $k\times n$ integer primitive matrix $\bf{A}$ (i.e., a matrix can be extended to an $n\times n$ unimodular matrix over the integers) with the maximal absolute value of entries $\|\bf{A}\|$ bounded by {an integer} $λ$ from above, we study the probability that the $m\times n$ matrix extended from $\bf{A}$ by appending other $m-k$ row vectors of dimension $n$ with entries chosen randomly and independently from the uniform distribution over $\{0, 1,\ldots, λ-1\}$ is still primitive. We present a complete and rigorous proof of a lower bound on the probability, which is at least a constant for fixed $m$ in the range $[k+1, n-4]$. As an application, we prove that there exists a fast Las Vegas algorithm that completes a $k\times n$ primitive matrix $\bf{A}$ to an $n\times n$ unimodular matrix within expected $\tilde{O}(n^ω\log \|\bf{A}\|)$ bit operations, where $\tilde{O}$ is big-$O$ but without log factors, $ω$ is the exponent on the arithmetic operations of matrix multiplication.
Autoencoding Under Normalization Constraints
Venue: arXiv cs.LG 2023
Year: 2023

Likelihood is a standard estimate for outlier detection. The specific role of the normalization constraint is to ensure that the out-of-distribution (OOD) regime has a small likelihood when samples are learned using maximum likelihood. Because autoencoders do not possess such a process of normalization, they often fail to recognize outliers even when they are obviously OOD. We propose the Normalized Autoencoder (NAE), a normalized probabilistic model constructed from an autoencoder. The probability density of NAE is defined using the reconstruction error of an autoencoder, which is differently defined in the conventional energy-based model. In our model, normalization is enforced by suppressing the reconstruction of negative samples, significantly improving the outlier detection performance. Our experimental results confirm the efficacy of NAE, both in detecting outliers and in generating in-distribution samples.